The AWS MCP Servers are a set of dedicated servers based on the Model Context Protocol, offering various AWS-related functions, including document retrieval, knowledge base query, CDK best practices, cost analysis, image generation, etc., aiming to enhance the integration of AI applications with AWS services through a standardized protocol.

pg-aiguide is an AI-optimized PostgreSQL knowledge base that provides semantic search for official documentation, best practice skills, and extended ecosystem documentation for AI programming assistants, helping to generate higher-quality PostgreSQL code.

A persistent memory server based on a knowledge graph, supporting cross - session storage and retrieval of user information

Obsidian's MCP Tools plugin provides AI applications with the ability to securely access the knowledge base

The Biel.ai MCP server connects the IDE to product documentation and enables AI tools to access and search the company's knowledge base through RAG technology, providing intelligent code completion and technical Q&A.

The project aims to connect RAG applications to open-webui through the MCP protocol to achieve model context interaction, including basic PoC verification, knowledge base integration, and client docking.

MCP is an open-source protocol for standardizing LLM interactions, providing a unified framework for connecting data sources, obtaining context, using tools, and executing standard prompts. Project examples demonstrate how to build MCP servers and clients to implement knowledge base chatbot functionality.

An MCP server that provides semantic search and resource access for the Obsidian knowledge base

A code knowledge management tool based on vector embedding, used to maintain and query code library knowledge, supporting local storage and RAG enhancement.

Memex is a tool based on the Model Context Protocol (MCP) for analyzing web page content and adding it to a knowledge base, supporting Q&A interactions via the Claude API.

An MCP memory server based on the Qdrant vector database, providing knowledge graph and semantic search capabilities.

The Logseq MCP Server is a model context protocol server that provides direct integration with Logseq knowledge bases, enabling LLMs to interact programmatically with Logseq graphs.

Mobi MCP Server is a service based on the Model Context Protocol, used to enable the interaction between AI models and the Mobi knowledge management platform, providing functions such as search, data retrieval, content creation, and version control.

A deep research MCP service based on Ollama, realizing automated web search and knowledge synthesis through local LLM models

An ATT&CK knowledge base query service based on the MCP protocol, providing functions for retrieving attack techniques and tactics, querying mitigation measures, and detection methods.

A local multi - expert intelligent agent scheduling system that supports automatic generation of experts, tool calls, and knowledge base expansion, used for a graduation project.

A knowledge graph management server based on the MCP protocol, supporting multiple startup methods and complete knowledge graph operation functions, including experimental short story generation capabilities.

An MCP server that connects to the Obsidian knowledge base. Through the REST API plugin, AI agents can perform complex knowledge discovery and analysis tasks, supporting functions such as advanced search, regular expression matching, and full - text retrieval.

Memory Bank is a project knowledge management system based on the MCP protocol that automatically generates structured documents through AI to help teams efficiently manage project knowledge.

The BioContextAI Knowledge Base MCP is a model context protocol server designed for biomedical research that provides a standardized connection layer between AI systems and biomedical resources, supporting integrated access to multiple biomedical databases and tools.

This project implements an MCP server as a bridge between AI assistants and the Outline document service, providing functions such as document search, management, creation, and editing.

Repo GraphRAG MCP Server is a service based on the MCP protocol that uses LightRAG and Tree - sitter to build a knowledge graph from code and text documents and provides functions such as question - answering and implementation planning.

A memory server implementing the Model Context Protocol (MCP), providing persistent memory storage and retrieval functions based on a knowledge graph for large language models.

A knowledge graph server based on Neo4j, integrating AI models and the MCP protocol, supporting dynamic knowledge management and semantic search.

This project provides a server based on the Model Context Protocol (MCP) that supports remote execution of AppleScript and JavaScript automation scripts on macOS. It includes a rich knowledge base of predefined scripts and can control macOS applications and system functions.

An implementation of a knowledge graph management MCP server based on Neo4j, providing standard interfaces for the storage and retrieval of graph databases.

An HTTP server based on the MCP protocol, providing AGENTS.md knowledge management, structured task tracking, version history recording, and scratchpad functions, supporting multi - user collaboration and AI sub - agent calls.

This project is a document processing server based on the Model Context Protocol (MCP) standard. By building a vector database and an MCP interface, it enables AI assistants to access external document resources and break through the knowledge limitations of large language models. The project includes two major components: a document processing pipeline and an MCP server. It supports multiple embedding models and file formats and can be applied to scenarios such as querying the latest technical documents and understanding private code libraries.

MEMEX is a knowledge management server based on the MCP protocol, used to manage technical guides and AI context, providing functions for creation, search, update, and deletion, and supporting semantic search and integration with Claude AI

The Claudesidian MCP Server is a service project that interacts with the Obsidian knowledge base. It provides functions such as note management, web scraping, and memory retrieval, and supports local deployment and extended development.

The MCP Neo4j Knowledge Graph Memory Server is an enhanced memory storage system based on the Neo4j graph database, providing high-performance knowledge graph storage and retrieval capabilities for AI assistants.

The privateGPT MCP Server is a server implementation based on the Model Context Protocol (MCP), allowing users to seamlessly integrate with the privateGPT API through an MCP client to achieve functions such as chat, knowledge source management, and permission control.

ObsidianFetch is an MCP server dedicated to retrieving and displaying information from the Obsidian knowledge base. It solves the problems of slow response and cumbersome operations of existing servers, and adds the functions of automatically processing links and displaying backlinks.

RDF Explorer is a server based on the MCP protocol, providing knowledge graph exploration and analysis functions in local file or SPARQL endpoint modes, supporting natural language queries and SPARQL query conversion.

The WikiJS MCP Server is a tool that connects Claude to the Wiki.js knowledge base, allowing Claude to search, read, update, and create Wiki.js document pages for automated management of the team's knowledge base.

The Insights Knowledge Base (IKB) MCP Server is a free, plug-and-play knowledge base project with over 10,000 high-quality insight reports built-in. It supports local secure storage and private document processing. The project requires no configuration, is free forever, and is committed to updating report resources weekly.

Context Lens is a local semantic search tool that can convert any content into a searchable knowledge base, enabling AI assistants to understand the meaning rather than just match keywords. It uses a built - in LanceDB vector database, supports local files, GitHub repositories, and URL content, does not require an API key or cloud service, and processes data completely locally.

This project includes two servers based on the Model Context Protocol (MCP): an IoT device control server and a memory management server. The IoT server provides device control, status query, and real-time update functions, suitable for scenarios such as smart homes and industrial IoT; the memory management server provides long-term memory storage and semantic search functions, suitable for fields such as conversation history and knowledge management.

The Feishu Docs MCP Server supports accessing Feishu knowledge bases and cloud documents through the MCP protocol and can be integrated into AI applications.

The Shared Knowledge MCP Server is a middleware that provides unified knowledge base services for multiple AI assistants, enabling efficient information retrieval and sharing through RAG technology.